Big data

Big Data management 101: everything you need to know

12 April 2021 • 3 min read

Big Data Management 101- Everything You Need to Know  (1)

Data, data everywhere. But not a useful insight in sight. That’s certainly how it feels for many organisations fighting against the tidal wave of information flooding their databases and businesses.

This data may be structured or unstructured. It may be sitting in a repository, gathering (virtual) dust. Or it may be constantly streaming into your business. If you don’t have the right tools to effectively work with that information, you could easily get lost at sea.

There are some ports in the storm though. Now, a raft of new tools and technologies can help you navigate your big data and find the best way to capitalise on your information.


The Time To Act Is Now


In the last two years, 90% of the world’s data was created. And more than $180 billion is spent every year on big data analysis. The catchphrase “data is the new corporate currency” is more relevant than ever. 


But the “big data” ethos is starting to change. According to Gartner: “Forward-looking data and analytics teams are pivoting from traditional AI techniques relying on ‘big’ data to a class of analytics that requires less, or ‘small’ and more varied.”


By, effectively, breaking your data down into manageable chunks, this provides businesses with the agility to “deal with disruptive change, radical uncertainty and the opportunities they bring,” Garter explains. And that’s a vital capability to ensure your business is “future ready”.


Big Data management tools and techniques


You need the right tools at every stage of your data journey to break things down and work smarter. Here are a handful of emerging techniques you may want to investigate:


  • #1 Get your data in order  Data quality is a key concern, especially if you want to adopt AI and ML techniques. Your data must be consistent and timely. If you put rubbish in, you’ll get rubbish out.  There have been several data platform advances to help you avoid this. No code data science initiatives like OpenAI, for example, reduce the requirement to code repetitive data processing tasks, allowing your data scientists to focus on value-added tasks. 

  • #2 Update your architecture  As data becomes increasingly complex, you need the right architecture to store and access this information. A data mesh could unlock your analytical data at scale. This architecture views each data domain as a product. And each data domain is assigned to a specific team to ingest, store and analyse that information.  Serverless is another option for your short real-time and near-real-time processes. Here, where you can run code without provisioning or managing your servers. Both AWS Lambda and Azure Functions are emerging serverless solutions.

  • #3 Model your data fast  Once you have your high-quality data in the right repository, you need to do something with it. To get these insights, you may need to invest heavily in new technologies, processes and people.  This is where Intelligence as a Service (IaaS) can help. You simply send your data and business models to an IaaS company and receive actionable insights back. Azure ML and Amazon Sagemaker are two examples in this space, helping companies for build and deploy ML models.

  • #4 Visualise your data  Once you have your insights, you need to provide the tools to help individuals understand and analyse those insights further. 

  • To achieve this, there are many data visualisation tools available. Google Data Studio, for example, is a free tool to transform your data into customisable dashboards and reports.


Don’t forget your standards


There are now more ‘things’ connected to the internet than there are people on Earth - with more than 24 billion connected devices expected to exist by 2030.


Like the early days of the internet, there are emergent and competing common big data standards to help us cope with the unimaginably vast quantities of data that will result. This includes the ISO/IEC 20547 standards, for example. 


These standards can help you make sense of the huge amount of data being generated, being able to store it, index it and search it. These are challenges that companies like Shodan and Terbine are now grappling with.


If you’d like to have a conversation about your data – whether it’s recommendations on the right tools and techniques for your organisation, or how to get the most out of the data you have you can contact us here

Big data

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